Hardware Platform Research Articles

TREAFET: Temperature-Aware Real-Time Task Scheduling for FinFET based Multicores

The recent shift in the VLSI industry from conventional MOSFET to FinFET for designing contemporary chip-multiprocessor (CMP) has noticeably improved hardware platforms’ computing capabilities, but at the cost of several thermal issues. Unlike the conventional MOSFET, FinFET devices experience a significant increase in circuit speed at a higher temperature, called temperature effect inversion (TEI), but higher temperature can also curtail the circuit lifetime due to self-heating effects (SHEs). These fundamental thermal properties of FinFET introduced a new challenge for scheduling time-critical tasks on FinFET-based multicores that how to exploit TEI towards improving performance while combating SHEs. In this work, TREAFET , a temperature-aware real-time scheduler, attempts to exploit the TEI feature of FinFET-based multicores in a time-critical computing paradigm. At first, the overall progress of individual tasks is monitored, tasks are allocated to the cores, and finally, a schedule is prepared. By considering the thermal profiles of the individual tasks and the current thermal status of the cores, hot tasks are assigned to the cold cores and vice-versa. Finally, the performance and temperature are balanced on-the-fly by incorporating a prudential voltage scaling towards exploiting TEI while guaranteeing the deadline and thermal safety. Moreover, TREAFET stimulates the average runtime frequency by employing an opportunistic energy-adaptive voltage spiking mechanism, in which energy saving during memory stalls at the cores is traded off during the time slice having the spiked voltage. Simulation results claim TREAFET maintains a safe and stable thermal status (peak temperature below 80 °C) and improves frequency up to 17% over the assigned value, which ensures legitimate time-critical performance for a variety of workloads while surpassing a state-of-the-art technique. The stimulated frequency in TREAFET also finishes the tasks early, thus providing opportunities to save energy by power gating the cores, and achieves a 24% energy delay product (EDP) gain on average.

Real-Time Simulation System for Small Scale Regional Integrated Energy Systems

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.

Low-Cost 3D Indoor Visible Light Positioning: Algorithms and Experimental Validation

Visible light technology presents significant advancement for indoor IoT applications. These systems offer enhanced bit rate transmission, enabling faster and reliable data transfer. Moreover, optical-based visible light systems facilitate improved location services within indoor environments. However, many of these systems still exhibit limited accuracy within several centimeters, even when relying on costly high-resolution cameras. This paper introduces a novel low-cost visible light system for 3D positioning, designed to enhance indoor positioning accuracy using low-resolution images. Initially, we propose a non-integer pixel (NI-P) algorithm to enhance precision without the need for higher-resolution images. This algorithm allows the system to identify the precise light spot coordinates on the low-resolution images, enabling accurate positioning. Subsequently, we present an algorithm leveraging the precise coordinate data from the previous step to determine the 3D position of objects even in front of errors in the measures. Benefiting from high accuracy, reduced cost, and low complexity, the proposed system is suitable for implementation on low-end hardware platforms, thereby increasing the versatility and feasibility of visible light technologies in indoor settings. Experimental results show an average 2D positioning error of 1.08 cm and 3D error within 1.4 cm at 2.3 m separation between the object and camera, achieved with an average positioning time of 20 ms on a low-end embedded device. Consequently, the proposed system offers fast and highly accurate indoor positioning and tracking capabilities, making it suitable for applications like mobile robots, automated guided vehicles, and indoor parking management. Furthermore, it is easy to deploy and does not require re-calibration.

Closed‐loop stability analysis of deep reinforcement learning controlled systems with experimental validation

AbstractTrained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU).

A method for disturbance identification in power quality based on cross-attention fusion of temporal and spatial features

The rapid growth of energy storage scale in power systems has posed higher demands on the monitoring and classification of power quality disturbances (PQDs). Therefore, a method based on cross-attention fusion of temporal and spatial features is proposed to improve the accuracy and robustness of fine classification of PQDs in this study.Firstly, utilizing the improved Sample Convolution and Interaction Model (SCINet) with its recursive downsampling-convolution-interaction architecture, and adding convolutional pooling layers to extract the temporal features of PQDs.Then, the improved VGG model is employed to extract the spatial features of PQDs. Finally, by introducing a cross-attention mechanism to capture the correlation and interaction between the temporal and spatial features, the representation ability of the features is enhanced to achieve disturbance recognition. This study is based on IEEE standards and mathematical models, and generates 25 types of PDQ data through Python simulation for training and testing.The proposed method achieves an average classification accuracy of 95.01 % in a high-noise environment of 20 dB, as well as compared with other mainstream deep learning models and verified through ablation study. Furthermore, the classification test of 10 types of PQDs sampled on a hardware platform demonstrates an average accuracy of 99.66 %, further validating the reliability of the proposed method.

A realizable chaotic system with interesting sets of equilibria, characteristics, and its underactuated predefined-time sliding mode control

This paper presents a new 4D chaotic system having both single-scroll and double-scroll, self-excited and hidden attractors with the variation of system parameters. The proposed system exhibits several equilibrium points and their associated attractors. There are four interseting sets of equilibria with self-excited and hidden attractors. The system characteristics are demonstrated using phase plots, bifurcation diagrams and Lyapunov exponents. Offset boosting, amplitude control, the coexistence of attractors and antimonotonicity are demonstrated by the new system. Further, a predefined-time sliding mode controller with three control inputs for a 4D system is designed for successful synchronization between the two identical master and slave systems. Such an underactuated controller for synchronization is seldom seen in the literature. The developed 4D chaotic system is realized first using the NI Multisim circuit simulator and then in an NI Academic RIO hardware platform. The outcomes validate the numerical results.

Switching-mode audio amplifier based on a ΔΣ A/D converter

The article describes the design and implementation of a modular hardware platform intended for testing and measuring various configurations of switching-mode audio amplifiers based on sigma-delta modulation. Two single-channel power amplifier modules were designed and manufactured, along with a stereophonic module serving as the basic source of modulated signals. Additionally, measurements were conducted on the fundamental parameters of the completed amplifier, such as harmonic distortion level, dynamic range, and output power. The developed platform serves as a foundation for modifications and further advancement in the technology of building switching-mode audio amplifiers.

Multistability and synchronicity of memristor coupled adaptive synaptic neuronal network

Memristor coupled neuronal networks can simulate the complex interactions between neurons in biological neuronal networks. These capture the current and synaptic transmission between neurons through mathematical equations, thus reflecting the signal transmission and integration in biological neuronal networks. This paper presents a five-dimensional isomorphic memristor coupled adaptive synaptic neuron (mCASN) network using a non-ideal memristor to couple two-dimensional adaptive synaptic neurons. The theoretical analysis of equilibrium points shows that the mCASN network has many different stability types. The complex parameters-dependent bifurcation behaviors are studied using various numerical methods, and the initial state-dependent coexistence behaviors and the riddled-like basins of attraction sensitive to initial states are further revealed, which proves that the coupled network has multistability. In addition, using the normalized average synchronization error method, we reveal that the mCASN network has synchronization characteristics controlled by parameters and initial states. The complete synchronization and lag synchronization induced by the network parameters, the internal variable of the coupled memristor, and the state variable of the sub-neuron are numerically analyzed. Finally, the mCASN network is implemented using an MCU-based digital hardware platform, and the numerical results are verified.

A Forest Fire Smoke Monitoring System Based on a Lightweight Neural Network for Edge Devices

Forest resources are one of the indispensable resources of the earth, which are the basis for the survival and development of human society. With the swift advancements in computer vision and artificial intelligence technology, the utilization of deep learning for smoke detection has achieved remarkable results. However, the existing deep learning models have poor performance in forest scenes and are difficult to deploy because of numerous parameters. Hence, we introduce an optimized forest fire smoke monitoring system for embedded edge devices based on a lightweight deep learning model. The model makes full use of the multi-scale variable attention mechanism of Transformer architecture to strengthen the ability of image feature extraction. Considering the needs of application scenarios, we propose an improved lightweight network model LCNet for feature extraction, which can reduce the parameters and enhance detecting ability. In order to improve running speed, a simple semi-supervised label knowledge distillation scheme is used to enhance the overall detection capability. Finally, we design and implement a forest fire smoke detection system on an embedded device, including the Jetson NX hardware platform, high-definition camera, and detection software system. The lightweight model is transplanted to the embedded edge device to achieve rapid forest fire smoke detection. Also, an asynchronous processing framework is designed to make the system highly available and robust. The improved model reduces three-fourths of the parameters and increases speed by 3.4 times with similar accuracy to the original model. This demonstrates that our system meets the precision demand and detects smoke in time.

Optimized decomposition and identification method for multiple power quality disturbances

AbstractThe complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability.

Bio-inspired computational memory model of the Hippocampus: An approach to a neuromorphic spike-based Content-Addressable Memory

The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems.

High Wideband Digital Oscilloscope Design

In most test applications, acquisition and analysis involving simultaneous processing of analog and digital signals. However, the bandwidth of most mainstream digital oscilloscopes is limited to 100 MHz, which is unable to meet the testing needs of high-frequency signals in complex electronic systems [1], and therefore, high-bandwidth digital oscilloscopes have emerged. Based on this background, this paper designs a digital oscilloscope hardware platform with high bandwidth by integrating FPGA and ARM technologies, aiming to meet the rigorous testing requirements of modern electronic systems. The FPGA module is based on the xc7s75fgga676 chip, which is mainly responsible for ADC control, data processing and frequency measurement functions. AM5708 is selected as the ARM module to realize the trigger, time base, amplitude and automatic setting functions of the oscilloscope. In order to ensure the accuracy and fidelity of waveform changes, the Sinc function interpolation method is used. This design further improves the acquisition bandwidth and processing speed on the basis of traditional MSO (Mixed Signal Oscilloscope) oscilloscopes, which is of great significance for the acquisition and processing of high-speed signals.

Progressive Web Apps in Cross-Platform Development: A Comparative Analysis and Evaluation

The evolution of app development methodologies has been significant, yet the discussion around cross-platform development remains pertinent. Apps must seamlessly function on both Android and iOS devices, accommodating diverse hardware configurations and platform versions. The proliferation of device categories beyond smartphones and tablets adds further complexity to achieving multi-platform compatibility. Despite the presence of multi-platform frameworks supported by both practical application and research, the challenge of efficiently developing apps for various targets persists. Progressive Web Apps (PWAs) potentially offer a solution to this ongoing quest for unifying technology. This paper aims to evaluate the role of PWAs in multi-platform development, exploring their foundational aspects and assessing the current landscape of possibilities. The evaluation includes a comparative analysis conducted in two phases. In the first phase, the different application development paradigms are analyzed with their advantages and disadvantages. In the second phase, the PWAs are compared to native and cross-hybrid apps based on launch time, installation size, and Largest Contentful Paint (LCP), with PWAs outperforming in all three metrics. By examining these aspects, the paper seeks to provide a complete understanding of PWAs' potential in cross-platform development. Key Words: Progressive Web Apps, Cross-Platform Development, App Development, Web Application, Multi-Platform Compatibility, Frameworks, Comparative Analysis, Performance Evaluation, Technology Unification, Mobile Devices, Android, iOS

Using HSV-based approach for detecting and grasping an object by the industrial mechatronic system

In term of the industrialization era, robot gradually appears in some production stages instead of worker. There is an irreversible tendency to deploy the image processing techniques into the field of robot control. In recent years, the vision-based techniques have reached to many considerable achievements. Most of them requires the complicated setup, specialized camera and professional operator for the process of burden computation. In this paper, an efficient solution based on the vision technique to detect and grasp an object in the indoor environment is introduced. The framework of this system which contains the geometrical constraints, theories of robot control and hardware platform, is described. To manipulate the task of detecting and grasping, the proposed method from calibration to visual estimation is mentioned in detail. From the results of both theoretical simulation and experiment, it is clearly stated that our approach is efficient, feasible and applicable.

Role of spatial coherence in diffractive optical neural networks.

Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations of DONNs have only been performed using coherent light. However, many real-world DONN applications require consideration of the spatial coherence properties of the optical signals. Here, we study the role of spatial coherence in DONN operation and performance. We propose a numerical approach to efficiently simulate DONNs under incoherent and partially coherent input illumination and discuss the corresponding computational complexity. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits to process light with varying spatial coherence.

Partitioned scheduling with safety-performance trade-offs in stochastic conditional DAG models

This paper is motivated by robotic systems that solve difficult real-world problems such as search and rescue (SAR) or precision agriculture 11This work was supported by NSF under Grant CNS-1932074.. These applications require robots to operate in complex, uncertain environments while maintaining safe interactions with human teammates within a specified level of performance. In this paper, we study the scheduling of real-time applications on heterogeneous hardware platforms inspired by such contexts. To capture the stochasticity due to unpredictable environments, we propose the stochastic heterogeneous parallel conditional DAG (SHPC-DAG) model, which extends the most recent HPC-DAG model in two regards. First, it uses conditional DAG nodes to model the execution of computational pipelines based on context, while the stochasticity of DAG edges captures the uncertain nature of a system’s environment or the reliability of its hardware. Second, considering the pessimism of deterministic worst-case execution time (WCET), it uses probability distributions to model the execution times of subtasks (DAG nodes). We propose a new partitioning algorithm Least Latency Partitioned (LLP), which considers precedence constraints among nodes during the allocation process. Coupled with a scheduling algorithm that accounts for varying subtask criticality and constraints, the end-to-end latencies of safety-critical paths/nodes are then minimized. We use tasksets inspired by real robotics to demonstrate that our framework allows for efficient scheduling in complex computational pipelines, with more flexible representation of timing constraints, and ultimately, safety-performance tradeoffs.

Comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography.

Photoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Notably, our framework can achieve an effective rate of times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

Retinomorphic X-ray detection using perovskite with hydrion-conductive organic cations

X-ray detection is crucial across various sectors, but traditional techniques face challenges such as inefficient data transmission, redundant sensing, high power consumption, and complexity. The innovative idea of a retinomorphic X-ray detector shows great potential. However, its implementation has been hindered by the absence of active layers capable of both detecting X-rays and serving as memory storage. In response to this critical gap, our study integrates hybrid perovskite with hydrion-conductive organic cations to develop a groundbreaking retinomorphic X-ray detector. This novel device stands at the nexus of technological innovation, utilising X-ray detection, memory, and preprocessing capabilities within a single hardware platform. The core mechanism underlying this innovation lies in the transport of electrons and holes within the metal halide octahedral frameworks, enabling precise X-ray detection. Concurrently, the hydrion movement through organic cations endows the device with short-term resistive memory, facilitating rapid data processing and retrieval. Notably, our retinomorphic X-ray detector boasts an array of formidable features, including reconfigurable short-term memory, a linear response curve, and an extended retention time. In practical terms, this translates into the efficient capture of motion projections with minimal redundant data, achieving a compression ratio of 18.06% and an impressive recognition accuracy of up to 98.6%. In essence, our prototype represents a paradigm shift in X-ray detection technology. With its transformative capabilities, this retinomorphic hardware is poised to revolutionize the existing X-ray detection landscape.

P‐77: A Novel Frequency Doubling Technology with Adaptive Dither

In this paper, in order to improve the refresh rate of large‐size LCD displays, a new optimized driving scheme combined with new GOA timing is implemented in hardware platform, which is called adaptive scanning for frequency multiplication (ASF) technology, and it can be applied to TV, MNT, NB and other fields. Firstly, this paper introduces the basic principle of Dual Line Gate technology (DLG), which is widely used, and analyzes its advantages and disadvantages. And then ASF technology is proposed to solve the disadvantages of DLG technology. The new optimized driving scheme mainly applied inter‐frame adaptive dither algorithm(ADA), odd and even frame interlaced compensation algorithm(OEC) and application mode for intelligent detection of pictures. A prototype driving circuit was fabricated, and various detection tests have been implemented. The results show the refresh rate is doubled without increasing hardware resources, and this technology avoids the loss of image quality.

Coupled Memristor Oscillators for Neuromorphic Locomotion Control: Modeling and Analysis.

The recent surge of interest in brain-inspired architectures along with the development of nonlinear dynamical electronic devices and circuits has enabled energy-efficient hardware realizations of several important neurobiological systems and features. Central pattern generator (CPG) is one such neural system underlying the control of various rhythmic motor behaviors in animals. A CPG can produce spontaneous coordinated rhythmic output signals without any feedback mechanism, ideally realizable by a system of coupled oscillators. Bio-inspired robotics aims to use this approach to control the limb movement for synchronized locomotion. Hence, devising a compact and energy-efficient hardware platform to implement neuromorphic CPGs would be of great benefit for bio-inspired robotics. In this work, we demonstrate that four capacitively coupled vanadium dioxide (VO2) memristor-based oscillators can produce spatiotemporal patterns corresponding to the primary quadruped gaits. The phase relationships underlying the gait patterns are governed by four tunable bias voltages (or four coupling strengths) making the network programmable, reducing the complex problem of gait selection and dynamic interleg coordination to the choice of four control parameters. To this end, we first introduce a dynamical model for the VO2 memristive nanodevice, then perform analytical and bifurcation analysis of a single oscillator, and finally demonstrate the dynamics of coupled oscillators through extensive numerical simulations. We also show that adopting the presented model for a VO2 memristor reveals a striking resemblance between VO2 memristor oscillators and conductance-based biological neuron models such as the Morris-Lecar (ML) model. This can inspire and guide further research on implementation of neuromorphic memristor circuits that emulate neurobiological phenomena.